
import mesa
import numpy as np
import random
import networkx as nx 
import bisect
from pathlib import Path
import os
import sys
import csv
import pandas as pd

# Basic paths
src_path = str(Path.cwd().parent / "scripts")
sys.path.append(src_path)

project_path = Path().resolve().parent
csv_path = project_path / "data"

# Python file stored in script folder
from data_cleaning import *


'''
    Only Scenario 1
'''

num_reps = 10
rdfs = []

for i in range(1, num_reps+1):
    file_name = f"scenario1_rep{i}.csv"
    rdf = pd.read_csv(csv_path / file_name, na_filter=True)
    rdfs.append(rdf)


rdfs[0]


"""
    Run this only if you already have the data sets. Otherwise, next line.
"""
num_scen = 3
num_reps = 10

dfs = []
for j in range(1, num_scen+1):
    for i in range(1, num_reps+1):
        file_name = f"s{j}r{i}_analysis.csv"
        df = pd.read_csv(csv_path/file_name, index_col=0)
        df['Scenario'] = j
        df['Replication'] = i
        dfs.append(df)


"""
    Run this only when data sets aren't provided. Warning: it takes long to run!! 
"""
# num_scen = 3
# num_reps = 10
# dfs = []
# for j in range(1, num_scen+1):
#     for i in range(1, num_reps+1):
#         file_name = f"scenario{j}_rep{i}.csv"
#         df = process_data(csv_path / file_name)
#         df['Scenario'] = j
#         df['Replication'] = i
#         dfs.append(df)


dfs[10]


for i in range(1, num_scen*num_reps+1):
        df = dfs[i-1]
        # print('Scenario', df['Scenario'][0], 'Replication', df['Replication'][0])
        scen = df['Scenario'][0]
        rep = df['Replication'][0]
        df.to_csv(csv_path/f's{scen}r{rep}_analysis.csv')


import plotly.graph_objects as go

# Create dictionary of dataframes
df_dict = {}
row=0
for j in range(1, num_scen+1):
    for i in range(1, num_reps+1):
        key = f"scenario {j}"
        df = dfs[row]
        df_mean = df.groupby(['Cohort'], as_index=False).Edu_level.mean()
        if key in df_dict:
            df_dict[key].append(df_mean)
        else:
            df_dict[key] = [df_mean]
        row += 1

# Create list of replications and scenarios
replications = [f"rep {i}" for i in range(1, num_reps+1)]
scenarios = [f"scenario {j}" for j in range(1, num_scen+1)]

# Create figure
fig = go.Figure()

# Loop through each scenario
for i, scenario in enumerate(scenarios):
    # Get dataframes for current scenario
    dfs_scenario = df_dict[scenario]
    # Get color for current scenario
    color = f"hsl({(i/len(scenarios))*360}, 50%, 50%)"
    # Loop through each replication of the current scenario
    for j, df in enumerate(dfs_scenario):
        # Get name for current replication
        name = f"{scenario} - rep {j+1}"
        fig.add_trace(go.Scatter(x=df["Cohort"], y=df["Edu_level"],
                                  name=name,
                                  mode="lines+markers",
                                  line=dict(color=color),
                                  marker=dict(symbol=int(scenario[-1]))))

# Show figure
fig.show()


# Create dictionary of dataframes
df_dict = {}
for i in range(1, num_reps+1):
    key = f"rep {i}"
    df = rdfs[i-1]
    df_mean = df.groupby(['Step'], as_index=False).Education.mean()
    df_dict[key] = df_mean

# Create list of replications
replications = [f"rep {i}" for i in range(1, num_reps+1)]

# Create figure
fig = go.Figure()

# Loop through each replication
for i, replication in enumerate(replications):
    df_filtered = df_dict[replication]
    fig.add_trace(go.Scatter(x=df_filtered["Step"], y=df_filtered["Education"],
                              name=replication,
                              mode="lines+markers",
                              marker=dict(symbol=i+1)))

# Show figure
fig.show()


import plotly.graph_objects as go

# Create dictionary of dataframes
df_dict = {}
row=0
for j in range(1, num_scen+1):
    for i in range(1, num_reps+1):
        key = f"scenario {j}"
        df = dfs[row]
        am1 = df[(df['Mother_Edu'] < 5) & (df['Father_Edu'] <5)]
        counts = am1[(am1['Edu_level'] > am1['Mother_Edu']) & (am1['Edu_level'] > am1['Father_Edu'])].groupby('Cohort').size()
        totals = am1.groupby(['Cohort']).size()
        probs = counts/totals
        if key in df_dict:
            df_dict[key].append({'Cohort': probs.index, 'Probability': probs.values})
        else:
            df_dict[key] = [{'Cohort': probs.index, 'Probability': probs.values}]
        row += 1

# Create list of replications and scenarios
replications = [f"rep {i}" for i in range(1, num_reps+1)]
scenarios = [f"scenario {j}" for j in range(1, num_scen+1)]

# Create figure
fig = go.Figure()

# Loop through each scenario
for i, scenario in enumerate(scenarios):
    # Get dataframes for current scenario
    dfs_scenario = df_dict[scenario]
    # Get color for current scenario
    color = f"hsl({(i/len(scenarios))*360}, 50%, 50%)"
    # Loop through each replication of the current scenario
    for j, df in enumerate(dfs_scenario):
        # Get name for current replication
        name = f"{scenario} - rep {j+1}"
        fig.add_trace(go.Scatter(x=df["Cohort"], y=df["Probability"],
                                  name=name,
                                  mode="lines+markers",
                                  line=dict(color=color),
                                  marker=dict(symbol=int(j))))

# Show figure
fig.show()


absolute = pd.DataFrame(columns=["Scenario", "Replication", "Cohort", "Probability"])
row = 0
for j in range(1, num_scen+1):
    for i in range(1, num_reps+1):
        df = dfs[row]
        am1 = df[(df['Mother_Edu'] < 5) & (df['Father_Edu'] <5)]
        am2 = df[(df['Mother_Edu'] == 5) | (df['Father_Edu'] ==5)]
        counts = am1[(am1['Edu_level'] > am1['Mother_Edu']) & (am1['Edu_level'] > am1['Father_Edu'])].groupby('Cohort').size()
        counts2 = am2[(am2['Edu_level'] == 5)].groupby('Cohort').size()
        totals = am1.groupby(['Cohort']).size()
        totals2 = am2.groupby('Cohort').size()
        probs = counts/totals
        probs2 = counts2 / totals2
        probs3 = probs2 + probs
        temp = pd.DataFrame({"Scenario": j, "Replication": i, "Cohort": probs.index, "Probability": probs.values})
        temp2 = pd.DataFrame({"Scenario": j, "Replication": i, "Cohort": probs3.index, "Probability": probs3.values})
        absolute = pd.concat([absolute, temp], ignore_index=True)
        absolute2 = pd.concat([absolute, temp2], ignore_index=True)
        row +=1


# absolute.drop('Replication')
abs1 = absolute.groupby(['Scenario', 'Cohort']).agg({'Probability': ["mean", "median", "var", "sem", "std"]}).reset_index()
abs1['ci95_hi'] = abs1['Probability']['mean'] + 1.96* abs1['Probability']['sem']
abs1['ci95_lo'] = abs1['Probability']['mean'] - 1.96* abs1['Probability']['sem']
abs1


# abs1.columns = abs1.columns.droplevel()
abs1.columns = ['_'.join(col) for col in abs1.columns]
abs1.rename(columns = {'Scenario_':'Scenario', 'Cohort_':'Cohort', 'ci95_hi_': 'ci95_hi', 'ci95_lo_':'ci95_lo'}, inplace = True)
abs1


len(abs1['Probability_mean'])

202


import plotly.express as px

abs1["Scenario"] = abs1["Scenario"].astype(str)
fig = px.scatter(abs1, x='Cohort', y='Probability_mean', color='Scenario').update_traces(  
                 error_y={'type': 'data', 
                          'symmetric': False, 
                          'array': abs1['ci95_hi']-abs1['Probability_mean'], 
                           'arrayminus':abs1['Probability_mean']-abs1['ci95_lo']}
                )

fig.show()


import plotly.graph_objects as go

abs1["Scenario"] = abs1["Scenario"].astype(str)
scenarios = abs1["Scenario"].unique()

# Define a color map for the scenarios
color_map = {
    scenario: f"rgba({r}, {g}, {b}, 0.3)"
    for scenario, (r, g, b) in zip(scenarios, [(99, 109, 250), (239, 85, 59), (0, 204, 150)])
}




fig = go.Figure()

for scenario in scenarios:
    scenario_data = abs1[abs1["Scenario"] == scenario]
    
    fig.add_trace(go.Scatter(x=scenario_data['Cohort'],
                             y=scenario_data['Probability_mean'],
                             name=f'Scenario {scenario}',
                             mode='lines',
                             line=dict(color=color_map[scenario].replace('0.3', '1')))
                 )
    
    fig.add_trace(go.Scatter(x=scenario_data['Cohort'],
                             y=scenario_data['ci95_hi'],
                             name='Upper Bound of {}'.format(scenario),
                             mode='lines',
                             marker=dict(color="#444"),
                             line=dict(width=0),
                             showlegend=False))
    
    fig.add_trace(go.Scatter(x=scenario_data['Cohort'],
                             y=scenario_data['ci95_lo'],
                             name='Lower Bound of {}'.format(scenario),
                             mode='lines',
                             marker=dict(color="#444"),
                             line=dict(width=0),
                             showlegend=False,
                             fillcolor=color_map[scenario],
                             fill='tonexty'))
fig.update_layout(
    xaxis_title="Cohort",
    yaxis_title="Mean Probability",
    paper_bgcolor='rgba(0,0,0,0)',
    plot_bgcolor = 'rgba(0,0,0,0)',
    showlegend=False
)

fig.show()


# fig.write_image(project_path/"images/abs1_means2.png")
fig.write_html(project_path/"images/abs1_means.html")


# absolute.drop('Replication')
abs2 = absolute2.groupby(['Scenario', 'Cohort']).agg({'Probability': ["mean", "median", "var", "sem", "std"]}).reset_index()
abs2['ci95_hi'] = abs2['Probability']['mean'] + 1.96* abs2['Probability']['sem']
abs2['ci95_lo'] = abs2['Probability']['mean'] - 1.96* abs2['Probability']['sem']
abs2.columns = ['_'.join(col) for col in abs2.columns]
abs2.rename(columns = {'Scenario_':'Scenario', 'Cohort_':'Cohort', 'ci95_hi_': 'ci95_hi', 'ci95_lo_':'ci95_lo'}, inplace = True)
abs2


import plotly.express as px

abs2["Scenario"] = abs2["Scenario"].astype(str)
fig = px.scatter(abs2, x='Cohort', y='Probability_mean', color='Scenario').update_traces(  
                 error_y={'type': 'data', 
                          'symmetric': False, 
                          'array': abs2['ci95_hi']-abs2['Probability_mean'], 
                           'arrayminus':abs2['Probability_mean']-abs2['ci95_lo']}
                )

fig.show()


import plotly.graph_objects as go

# Create dictionary of dataframes
df_dict = {}
row = 0
for j in range(1, num_scen+1):
    for i in range(1, num_reps+1):
        key = f"scenario {j}"
        df = dfs[row]
        am1 = df[(df['Mother_Edu'] < 5) & (df['Father_Edu'] <5)]
        am2 = df[(df['Mother_Edu'] == 5) | (df['Father_Edu'] ==5)]
        counts = am1[(am1['Edu_level'] > am1['Mother_Edu']) & (am1['Edu_level'] > am1['Father_Edu'])].groupby('Cohort').size()
        counts2 = am2[(am2['Edu_level'] == 5)].groupby('Cohort').size()
        totals = am1.groupby(['Cohort']).size()
        totals2 = am2.groupby('Cohort').size()
        probs = counts/totals
        probs2 = counts2 / totals2
        probs3 = probs2 + probs
        if key in df_dict:
            df_dict[key].append({'Cohort': probs.index, 'Probability': probs.values, 'Cohort2': probs2.index, 'Probability2': probs2.values,
                                 'Probability3': probs3.values})
        else:
            df_dict[key] = [{'Cohort': probs.index, 'Probability': probs.values, 'Cohort2': probs2.index, 'Probability2': probs2.values,
                              'Probability3': probs3.values}]        
        row+=1

# Create list of replications and scenarios
replications = [f"rep {i}" for i in range(1, num_reps+1)]
scenarios = [f"scenario {j}" for j in range(1, num_scen+1)]

# Create figures
fig = go.Figure()
fig2 = go.Figure()
fig3 = go.Figure()

# Loop through each scenario
for i, scenario in enumerate(scenarios):
    # Get dataframes for current scenario
    dfs_scenario = df_dict[scenario]
    # Get color for current scenario
    color = f"hsl({(i/len(scenarios))*360}, 50%, 50%)"
    # Loop through each replication of the current scenario
    for j, df in enumerate(dfs_scenario):
        # Get name for current replication
        name = f"{scenario} - rep {j+1}"
        # Add traces to figures
        fig.add_trace(go.Scatter(x=df["Cohort"], y=df["Probability"],
                                  name=name,
                                  mode="lines+markers",
                                  line=dict(color=color),
                                  marker=dict(symbol=int(j))))
        fig2.add_trace(go.Scatter(x=df["Cohort2"], y=df["Probability2"],
                                   name=name,
                                   mode="lines+markers",
                                   line=dict(color=color),
                                   marker=dict(symbol=int(j))))
        fig3.add_trace(go.Scatter(x=df["Cohort"], y=df["Probability3"],
                                   name=name,
                                   mode="lines+markers",
                                   line=dict(color=color),
                                   marker=dict(symbol=int(j))))

# Show figures
fig.show()
fig2.show()
fig3.show()


dfs[0].columns

Index(['AgentID', 'Step', 'Age', 'Edu_level', 'Gender', 'Income', 'Generation',
       'Cohort', 'Weight', 'Capital', 'Brut', 'Child1', 'Child2', 'Mother',
       'Father', 'Partner', 'Education', 'Cultural', 'Economic', 'Social',
       'Child1_Edu', 'Child2_Edu', 'Child1_Income', 'Child2_Income',
       'Mother_Edu', 'Father_Edu', 'Mother_Income', 'Father_Income',
       'Spouse_Edu', 'Mother_Cultural', 'Mother_Economic', 'Mother_Social',
       'Father_Cultural', 'Father_Economic', 'Father_Social', 'Scenario',
       'Replication'],
      dtype='object')


s1r1.Cohort.unique()

array([ 7,  6,  5,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
       22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
       39, 40, 41, 42, 43, 44, 45, 46, 47,  4,  3, 48, 49, 50, 51, 52, 53,
       54, 55])


'''
    Make a function to create summary tables for each data set
'''
def summary_tab(df):
    ls = pd.DataFrame()
    row = 0
    for i in df.Cohort.unique():
        cht = df[df['Cohort'] == i]
        for j in range(1, 6):
            for k in range(1, 6):
                new = cht[(cht['Mother_Edu'] == j) | (cht['Father_Edu'] == k)]
                n = new.size
                avg = new['Edu_level'].mean()
                med = new['Edu_level'].median()
                ls.loc[row, 'Cohort'] = i
                ls.loc[row, 'Average Edu'] = avg
                ls.loc[row, 'Median Edu'] = med
                ls.loc[row, 'Total'] = n
                ls.loc[row, 'Mother Edu'] = j
                ls.loc[row, 'Father Edu'] = k
                row += 1
    return ls


ls_full = pd.DataFrame()
row = 0
for i in range(1, num_scen+1):
    for j in range(1,num_reps+1):
        df = dfs[row]
        df2 =  df[df['Generation'] != 1]
        df3 = summary_tab(df2)
        df3['Scenario'] = i
        df3['Replication'] = j
        ls_full = pd.concat([ls_full, df3], ignore_index=True)
        row +=1


# pd.set_option('display.max_rows', None)
ls_full


# pd.set_option("display.max_rows", 25)


agg_ls = ls_full.groupby(['Scenario', 'Cohort', 'Mother Edu', 'Father Edu']).agg({'Average Edu': ["mean", "sem", "std"], 'Median Edu': ['mean', 'sem', 'std'] }).reset_index()
agg_ls['ci95_hi_avg'] = agg_ls['Average Edu']['mean'] + 1.96* agg_ls['Average Edu']['sem']
agg_ls['ci95_lo_avg'] = agg_ls['Average Edu']['mean'] - 1.96* agg_ls['Average Edu']['sem']
agg_ls['ci95_hi_med'] = agg_ls['Median Edu']['mean'] + 1.96* agg_ls['Median Edu']['sem']
agg_ls['ci95_lo_med'] = agg_ls['Median Edu']['mean'] - 1.96* agg_ls['Median Edu']['sem']
agg_ls.columns = ['_'.join(col) for col in agg_ls.columns]
agg_ls.head(5)


agg_ls.rename(columns = {'Scenario_':'Scenario', 'Cohort_':'Cohort', 'Mother Edu_':'Mother Edu', 'Father Edu_':'Father Edu',
                         'ci95_hi_avg_': 'ci95_hi_avg', 'ci95_lo_avg_':'ci95_lo_avg',
                         'ci95_hi_med_': 'ci95_hi_med', 'ci95_lo_med_':'ci95_lo_med'}, inplace = True)

agg_ls['Parent Edu'] = agg_ls.apply(lambda x: [x['Mother Edu'], x['Father Edu']], axis=1)

agg_ls.head(5)


import plotly.express as px

agg_ls["Parent Edu"] = agg_ls["Parent Edu"].astype(str)
data = agg_ls[agg_ls['Scenario'] == 1]
fig = px.line(data, x='Cohort', y='Median Edu_mean', color='Parent Edu')
# .update_traces(  
#                  error_y={'type': 'data', 
#                           'symmetric': False, 
#                           'array': data['ci95_hi_med']-data['Median Edu_mean'], 
#                            'arrayminus':data['Median Edu_mean']-data['ci95_lo_med']}
#                 )

fig.show()


import plotly.express as px

agg_ls["Parent Edu"] = agg_ls["Parent Edu"].astype(str)
data = agg_ls[agg_ls['Scenario'] == 2]
fig = px.line(data, x='Cohort', y='Median Edu_mean', color='Parent Edu')
# .update_traces(  
#                  error_y={'type': 'data', 
#                           'symmetric': False, 
#                           'array': data['ci95_hi_med']-data['Median Edu_mean'], 
#                            'arrayminus':data['Median Edu_mean']-data['ci95_lo_med']}
#                 )

fig.show()


import plotly.express as px

agg_ls["Parent Edu"] = agg_ls["Parent Edu"].astype(str)
data = agg_ls[agg_ls['Scenario'] == 3]
fig = px.line(data, x='Cohort', y='Median Edu_mean', color='Parent Edu')
# .update_traces(  
#                  error_y={'type': 'data', 
#                           'symmetric': False, 
#                           'array': data['ci95_hi_med']-data['Median Edu_mean'], 
#                            'arrayminus':data['Median Edu_mean']-data['ci95_lo_med']}
#                 )

fig.show()

	Step	AgentID	Age	Education	Gender	Income	Spouse	Parents	Generation	Cohort	Children	Weight	Capital	Brut
0	0	0	22	4	M	383.912	NaN	[]	1	2	[525, 722]	[4, 9, 6]	[0.56, 0.456, 0]	NaN
1	0	1	59	2	F	572.317	NaN	[]	1	1	[]	[3, 7, 9]	[0.26, 0.608, 0]	NaN
2	0	2	46	2	F	207.210	NaN	[]	1	1	[]	[3, 4, 7]	[0.26, 0.181, 0]	NaN
3	0	3	31	1	M	524.950	NaN	[]	1	2	[]	[1, 7, 8]	[0.11, 0.558, 0]	NaN
4	0	4	30	3	F	208.252	NaN	[]	1	2	[633, 668]	[7, 5, 4]	[0.51, 0.195, 0]	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
148686	499	5328	10	4	M	679.790	NaN	[5301, 5276]	53	55	[]	[7, 7, 1]	[0.68, 0.722, 0.97]	32.0
148687	499	5329	10	5	M	773.909	NaN	[5303, 5293]	53	55	[]	[4, 3, 1]	[0.7, 0.629, 0.912]	31.0
148688	499	5330	8	4	M	782.493	NaN	[5303, 5293]	53	55	[]	[1, 4, 1]	[0.44, 0.685, 0.97]	32.0
148689	499	5331	8	5	M	702.780	NaN	[5306, 5300]	53	55	[]	[2, 7, 3]	[0.6, 0.747, 0.97]	32.0
148690	499	5332	6	5	M	393.503	NaN	[5306, 5300]	53	55	[]	[7, 8, 1]	[0.85, 0.443, 0.971]	33.0

	AgentID	Step	Age	Edu_level	Gender	Income	Generation	Cohort	Weight	Capital	...	Mother_Income	Father_Income	Mother_Cultural	Mother_Economic	Mother_Social	Father_Cultural	Father_Economic	Father_Social	Scenario	Replication
0	0	0	46	3	F	525.366	1	1	[9, 6, 7]	['0.57', '0.525', '0']	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	1
1	1	20	58	4	F	552.580	1	3	[4, 10, 9]	['0.56', '0.691', '0.413']	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	1
2	10	1	47	1	F	592.263	1	1	[10, 1, 7]	['0.2', '0.407', '0.904']	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	1
3	100	1	51	2	F	434.857	1	1	[6, 5, 4]	['0.32', '0.408', '0.827']	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	1
4	1000	53	60	5	F	513.299	4	6	[6, 8, 9]	['0.8', '0.577', '0.942']	...	721.298	753.854	0.60	0.586	0.828	0.55	0.565	0.697	2	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7232	995	54	62	4	M	614.759	4	6	[3, 1, 9]	['0.52', '0.423', '0.954']	...	711.882	653.918	0.60	0.801	0.804	0.54	0.572	0.570	2	1
7233	996	58	70	3	F	201.748	4	6	[7, 3, 7]	['0.51', '0.164', '0.907']	...	799.748	764.927	0.56	0.700	0.905	0.56	0.813	0.943	2	1
7234	997	54	62	5	M	775.373	5	6	[7, 3, 1]	['0.85', '0.63', '0.889']	...	796.356	380.924	1.00	0.547	0.404	1.00	0.429	0.275	2	1
7235	998	55	64	4	F	484.957	5	6	[9, 4, 1]	['0.76', '0.424', '0.843']	...	502.834	488.990	0.75	0.629	0.613	0.75	0.336	0.638	2	1
7236	999	54	62	3	F	682.823	4	6	[3, 4, 8]	['0.39', '0.597', '0.823']	...	621.984	57.366	0.42	0.583	0.524	0.48	0.054	0.358	2	1

	Scenario	Cohort	Probability					ci95_hi	ci95_lo
			mean	median	var	sem	std
0	1	3	0.368278	0.372768	0.003509	0.018733	0.059239	0.404995	0.331562
1	1	4	0.394438	0.373190	0.008489	0.029136	0.092137	0.451545	0.337331
2	1	5	0.390056	0.390476	0.005815	0.024114	0.076255	0.437319	0.342792
3	1	6	0.405786	0.398630	0.004566	0.021367	0.067569	0.447666	0.363906
4	1	7	0.401345	0.385705	0.009663	0.031086	0.098303	0.462273	0.340416
...	...	...	...	...	...	...	...	...	...
197	3	64	0.500000	0.500000	NaN	NaN	NaN	NaN	NaN
198	3	65	NaN	NaN	NaN	NaN	NaN	NaN	NaN
199	3	66	0.500000	0.500000	NaN	NaN	NaN	NaN	NaN
200	3	67	NaN	NaN	NaN	NaN	NaN	NaN	NaN
201	3	69	0.500000	0.500000	NaN	NaN	NaN	NaN	NaN

	Scenario	Cohort	Probability_mean	Probability_median	Probability_var	Probability_sem	Probability_std	ci95_hi	ci95_lo
0	1	3	0.368278	0.372768	0.003509	0.018733	0.059239	0.404995	0.331562
1	1	4	0.394438	0.373190	0.008489	0.029136	0.092137	0.451545	0.337331
2	1	5	0.390056	0.390476	0.005815	0.024114	0.076255	0.437319	0.342792
3	1	6	0.405786	0.398630	0.004566	0.021367	0.067569	0.447666	0.363906
4	1	7	0.401345	0.385705	0.009663	0.031086	0.098303	0.462273	0.340416
...	...	...	...	...	...	...	...	...	...
197	3	64	0.500000	0.500000	NaN	NaN	NaN	NaN	NaN
198	3	65	NaN	NaN	NaN	NaN	NaN	NaN	NaN
199	3	66	0.500000	0.500000	NaN	NaN	NaN	NaN	NaN
200	3	67	NaN	NaN	NaN	NaN	NaN	NaN	NaN
201	3	69	0.500000	0.500000	NaN	NaN	NaN	NaN	NaN

	Scenario_	Cohort_	Mother Edu_	Father Edu_	Average Edu_mean	Average Edu_sem	Average Edu_std	Median Edu_mean	Median Edu_sem	Median Edu_std	ci95_hi_avg_	ci95_lo_avg_	ci95_hi_med_	ci95_lo_med_
0	1	3.0	1.0	1.0	1.805588	0.125877	0.398057	1.2	0.133333	0.421637	2.052307	1.558870	1.461333	0.938667
1	1	3.0	1.0	2.0	2.405332	0.089751	0.283817	2.0	0.149071	0.471405	2.581244	2.229421	2.292180	1.707820
2	1	3.0	1.0	3.0	3.160301	0.073470	0.232331	3.3	0.152753	0.483046	3.304301	3.016300	3.599395	3.000605
3	1	3.0	1.0	4.0	3.436475	0.068618	0.216990	4.0	0.000000	0.000000	3.570967	3.301983	4.000000	4.000000
4	1	3.0	1.0	5.0	3.553121	0.076782	0.242806	4.1	0.100000	0.316228	3.703614	3.402628	4.296000	3.904000

Title: Analysis of Intergenerational Mobility in Education¶

Raw Data¶

Data Cleaning¶

Save CSV¶

Data Exporation¶

Per Cohort¶

Per Time Step (Raw Data)¶

Absolute Mobility¶

Method 1¶

Method 2¶

Relative Mobility¶

Who Benefits?¶

Plot¶

End¶

	Scenario	Cohort	Probability_mean	Probability_median	Probability_var	Probability_sem	Probability_std	ci95_hi	ci95_lo
0	1	3	0.368278	0.372768	0.003509	0.018733	0.059239	0.404995	0.331562
1	1	4	0.394438	0.373190	0.008489	0.029136	0.092137	0.451545	0.337331
2	1	5	0.390056	0.390476	0.005815	0.024114	0.076255	0.437319	0.342792
3	1	6	0.405786	0.398630	0.004566	0.021367	0.067569	0.447666	0.363906
4	1	7	0.401345	0.385705	0.009663	0.031086	0.098303	0.462273	0.340416
5	1	8	0.390206	0.403125	0.004846	0.022014	0.069614	0.433354	0.347059
6	1	9	0.379384	0.368571	0.005313	0.023051	0.072893	0.424564	0.334205
7	1	10	0.424142	0.448427	0.006466	0.025429	0.080412	0.473982	0.374302
8	1	11	0.376345	0.350000	0.006906	0.026278	0.083100	0.427851	0.324840
9	1	12	0.361747	0.344130	0.008393	0.028971	0.091613	0.418529	0.304964
10	1	13	0.373620	0.367500	0.006264	0.025029	0.079148	0.422677	0.324564
11	1	14	0.358438	0.395722	0.007461	0.027315	0.086377	0.411975	0.304901
12	1	15	0.372255	0.387500	0.007720	0.027785	0.087863	0.426713	0.317797
13	1	16	0.336267	0.363971	0.010827	0.032905	0.104053	0.400760	0.271774
14	1	17	0.379014	0.351471	0.013408	0.036617	0.115792	0.450783	0.307245
15	1	18	0.373497	0.365152	0.010810	0.032879	0.103972	0.437940	0.309055
16	1	19	0.385546	0.375940	0.011606	0.034067	0.107730	0.452317	0.318774
17	1	20	0.316154	0.312500	0.005604	0.023673	0.074861	0.362554	0.269755
18	1	21	0.358388	0.374783	0.006148	0.024794	0.078406	0.406985	0.309791
19	1	22	0.326568	0.306624	0.010673	0.032670	0.103311	0.390601	0.262536
20	1	23	0.411954	0.434874	0.007781	0.027894	0.088209	0.466627	0.357282
21	1	24	0.404165	0.378676	0.005027	0.022421	0.070902	0.448110	0.360219
22	1	25	0.342149	0.340336	0.008010	0.028302	0.089497	0.397620	0.286678
23	1	26	0.374419	0.383333	0.019729	0.044418	0.140461	0.461478	0.287360
24	1	27	0.334700	0.325855	0.006639	0.025766	0.081480	0.385202	0.284198
25	1	28	0.383652	0.368056	0.007677	0.027707	0.087616	0.437957	0.329348
26	1	29	0.418597	0.414216	0.005686	0.023845	0.075406	0.465334	0.371860
27	1	30	0.343867	0.386829	0.009010	0.030016	0.094919	0.402698	0.285035
28	1	31	0.393971	0.382353	0.007360	0.027128	0.085788	0.447143	0.340799
29	1	32	0.405834	0.386752	0.005904	0.024297	0.076835	0.453457	0.358212
30	1	33	0.356166	0.339286	0.009133	0.030221	0.095567	0.415399	0.296933
31	1	34	0.364223	0.378655	0.007767	0.027870	0.088132	0.418848	0.309598
32	1	35	0.327122	0.337719	0.010469	0.032356	0.102319	0.390540	0.263704
33	1	36	0.392030	0.326471	0.034807	0.058997	0.186565	0.507664	0.276396
34	1	37	0.388676	0.393382	0.014094	0.037541	0.118716	0.462258	0.315095
35	1	38	0.389266	0.333333	0.028126	0.053034	0.167709	0.493213	0.285319
36	1	39	0.326357	0.288889	0.014561	0.038159	0.120668	0.401148	0.251567
37	1	40	0.352493	0.333333	0.006833	0.026140	0.082663	0.403728	0.301258
38	1	41	0.340793	0.302778	0.018519	0.043034	0.136084	0.425139	0.256447
39	1	42	0.306175	0.300000	0.013357	0.036548	0.115574	0.377808	0.234542

	Cohort	Average Edu	Median Edu	Total	Mother Edu	Father Edu	Scenario	Replication
0	7.0	3.888889	4.5	666.0	1.0	1.0	1	1
1	7.0	3.954545	4.0	814.0	1.0	2.0	1	1
2	7.0	3.950000	4.0	1480.0	1.0	3.0	1	1
3	7.0	4.137255	4.0	1887.0	1.0	4.0	1	1
4	7.0	4.343750	5.0	2368.0	1.0	5.0	1	1
5	7.0	3.285714	3.0	518.0	2.0	1.0	1	1
6	7.0	3.812500	4.0	592.0	2.0	2.0	1	1
7	7.0	3.882353	4.0	1258.0	2.0	3.0	1	1
8	7.0	4.200000	4.0	1665.0	2.0	4.0	1	1
9	7.0	4.387097	5.0	2294.0	2.0	5.0	1	1
10	7.0	3.785714	4.0	1554.0	3.0	1.0	1	1
11	7.0	4.000000	4.0	1554.0	3.0	2.0	1	1
12	7.0	3.980000	4.0	1850.0	3.0	3.0	1	1
13	7.0	4.146667	4.0	2775.0	3.0	4.0	1	1
14	7.0	4.310811	4.5	2738.0	3.0	5.0	1	1
15	7.0	3.945946	4.0	1369.0	4.0	1.0	1	1
16	7.0	4.200000	4.0	1295.0	4.0	2.0	1	1
17	7.0	4.113208	4.0	1961.0	4.0	3.0	1	1
18	7.0	4.288136	5.0	2183.0	4.0	4.0	1	1
19	7.0	4.394366	5.0	2627.0	4.0	5.0	1	1
20	7.0	4.197368	4.0	2812.0	5.0	1.0	1	1
21	7.0	4.347222	4.5	2664.0	5.0	2.0	1	1
22	7.0	4.304878	4.0	3034.0	5.0	3.0	1	1
23	7.0	4.314607	4.0	3293.0	5.0	4.0	1	1
24	7.0	4.390000	5.0	3700.0	5.0	5.0	1	1
25	6.0	2.850000	3.0	740.0	1.0	1.0	1	1
26	6.0	2.944444	3.0	666.0	1.0	2.0	1	1
27	6.0	3.647059	4.0	1258.0	1.0	3.0	1	1
28	6.0	3.444444	4.0	1998.0	1.0	4.0	1	1
29	6.0	4.213115	5.0	2257.0	1.0	5.0	1	1
30	6.0	2.833333	3.0	666.0	2.0	1.0	1	1
31	6.0	3.277778	3.5	666.0	2.0	2.0	1	1
32	6.0	3.812500	4.0	1184.0	2.0	3.0	1	1
33	6.0	3.500000	4.0	1850.0	2.0	4.0	1	1
34	6.0	4.229508	5.0	2257.0	2.0	5.0	1	1
35	6.0	3.500000	4.0	962.0	3.0	1.0	1	1
36	6.0	3.666667	4.0	888.0	3.0	2.0	1	1
37	6.0	4.166667	4.0	1332.0	3.0	3.0	1	1
38	6.0	3.722222	4.0	1998.0	3.0	4.0	1	1
39	6.0	4.365079	5.0	2331.0	3.0	5.0	1	1